AI/ML based solutions are becoming ubiquitous, solving many critical problems impacting people’s lives. Banks and financial institutions rely on AI based solutions to improve user experience and take lending decisions. Insurance companies use AI to identify policy claim frauds. Recruiters leverage them to identify the best candidates. With the pervasiveness and the black box nature of these complex models, there is a growing need for models and solutions to be responsible and accountable.
Responsible AI develops artificial intelligence in a way that is inclusive and understandable by users. It provides explanation to models that make AI trustworthy, accountable and bias-free. This improves user confidence and ensures regulatory compliance. It allows all stakeholders to understand how the model predictions change with different inputs. Regulators can check for biased and discriminatory practices and decision makers can harness the potential of AI while maintaining the risks.
Mphasis Responsible AI is an end-to-end framework that enables companies to develop and deploy robust, interpretable, explainable, bias-free, auditable, and privacy preserving AI through a unique user experience and design thinking engagement. This makes the system trustworthy, thereby improving customer experience, reducing liability risk and ensuring regulatory compliance.
The Responsible AI components are generic and modular, enhancing scalability and repeatability across several use cases. For example, global and local explanation module understands internal logic and model limitations; bias identification and mitigation module assures model fairness; PII redaction module preserves privacy and so on. The ability to log experiments and model versions allow for explanation-accuracy trade-off analysis and auditability. Mphasis Responsible AI framework is fully integrated with PACE-ML, Mphasis’ proprietary MLOps framework for easy deployment.
High performing: Machine learning models achieve high level of performance in terms of identified metrics (such as accuracy, sensitivity, specificity, F1 score, log-loss, etc.)
Interpretable: A cause and effect can be observed where a human is able to predict the change in output given a change in the models’ input
Explainable: The internal mechanics of models can be explained in human terms
Auditable: Models’ actions and the attributes driving them are recorded with integrity and readily available for scrutiny
Bias Free: The models are impartial; they work without giving undue advantage/disadvantage to any class
Privacy Preserving: The models are free from any PII to respect privacy of all users
Improves model fairness: Allows developers to check if AI predictions discriminate against a class. This helps to mitigate biases and results in fair outcomes.
Ensures regulatory compliance: Increases transparency of AI models and helps understand reasons for a negative outcome.
Improves confidence in critical business decisions: Useful while the AI models take critical decisions that impact liabilities of the organization.
Simplifies model governance: Various steps in model development are available for scrutiny throughout its lifecycle. This includes post-deployment or even retired models.
Increases user trust: Produces human-centered intuitive explanation, thereby building user satisfaction and trust.
Eases deployment: Leverages pre-built components that offer fast deployment and customization to suit varied business contexts and requirements.
Cloud-agnostic and hybrid friendly: Can be deployed on-prem and any private or public cloud.
Flexible integration: Stand-alone deployment to explain AI models in production or integrated with other solutions.
Through AWS Marketplace, Mphasis offers AI solutions that deliver immediate results and ROI in critical enterprise business processes and operations. These solutions can be deployed with the speed and security provided by AWS. Our presence in the AWS marketplace has facilitated the expanding global footprint of Mphasis AI offerings, helping developers get hands-on experience on AI algorithms and layer AI based solutions in enterprise applications. Combined with AWS services, these AI solutions help in simplifying data experimentation, extract deeper insights from data estates and improve productivity for a variety of use cases.
Our current Responsible AI listings are: